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Compressive Sensing on Three Dimensional SFCW Ground-Penetrating Radar

Álvarez Justo, Jon
Master thesis
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https://hdl.handle.net/11250/2778138
Utgivelsesdato
2020
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  • Institutt for elektroniske systemer [1865]
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Stepped-frequency continuous-wave ground-penetrating radars (SFCW GPRs) are characterized

by relatively low data acquisition speed caused by stepwise slow scanning of the frequency

spectrum. The improvement of the imaging speed in SFCW GPR mostly relies on reducing

the data acquisition time. In this thesis, a paradigm of compressive sensing (CS) is explored

in order to minimize the acquisition time in three dimensional SFCW GPRs by reducing the

amount of real-time acquired data below the Nyquist rate by the use of the likely spatial sparsity

of the underground. This work analyzes the reconstructions provided by different convex and

greedy sparse recovery algorithms. Specifically, Basis Pursuit (BP), Matching Pursuit (MP),

Orthogonal Matching Pursuit (OMP), Compressive Sampling Matching Pursuit (CoSaMP),

Generalized Orthogonal Matching Pursuit (GOMP), Backtracking Iterative Hard Thresholding

(BIHT) and Regularized Orthogonal Matching Pursuit (ROMP). Data sets with synthetic underground mines are accurately reconstructed for compression ratios up to 85% causing that

the data acquisition time is reduced 6.67 times. In addition, the results show that for the data

sets of synthetic mines degraded by uniform noise, the proposed software compressive SFCW

GPR system implemented in MATLAB can be used for effective noise-removal filtering. Finally,

a real data set obtained in a bridge survey is precisely recovered for compression ratios up to 40%.
 
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